Phenomenological classification of the Zwicky Transient Facility astronomical event alerts

TL;DR

This paper introduces ACAI, a deep-learning framework for classifying astronomical transient alerts from the Zwicky Transient Facility, enabling efficient filtering and early identification of interesting events.

Contribution

The paper presents ACAI, an open-source AI system with five classifiers for phenomenological classification of ZTF alerts, enhancing real-time alert stream filtering.

Findings

ACAI achieves effective classification performance in real-time settings.

The framework supports early detection of supernova-like and anomalous events.

Deployment results demonstrate practical utility in astronomical alert processing.

Abstract

The Zwicky Transient Facility (ZTF), a state-of-the-art optical robotic sky survey, registers on the order of a million transient events - such as supernova explosions, changes in brightness of variable sources, or moving object detections - every clear night, and generates associated real-time alerts. We present Alert-Classifying Artificial Intelligence (ACAI), an open-source deep-learning framework for the phenomenological classification of ZTF alerts. ACAI uses a set of five binary classifiers to characterize objects which, in combination with the auxiliary/contextual event information available from alert brokers, provides a powerful tool for alert stream filtering tailored to different science cases, including early identification of supernova-like and anomalous transient events. We report on the performance of ACAI during the first months of deployment in a production setting.